I am new to Pandas. Would like to know how to apply a function to two columns in a dataframe and map the output from the function to a new column in the dataframe. Is this at all possible with pandas syntax or should I resort to native Python to iterate over the rows in the dataframe columns to generate the new column?
a b
1 2
3 1
2 9
Question is how to get, for example, the multiplication of the two numbers in a new column c
a b c
1 2 2
3 1 3
2 9 18
You can do with pandas.
For example:
def funcMul(row):
return row['a']*row['b']
Then,
df['c'] = df.apply(funcMul,1)
Output:
a b c
0 1 2 2
1 3 1 3
2 2 9 18
You can do the following with pandas
import pandas as pd
def func(r):
return r[0]*r[1]
df = pd.DataFrame({'a':[1,2,3], 'b':[4,5,6]})
df['c'] = df.apply(func, axis = 1)
Also, here is the official documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html
apply + lambda is a bad practice. Just saying.
The comment by harvpan shows the simplest way to achieve your specific example, but here is a generic way to do what you asked:
def functionUsedInApply(row):
""" The function logic for the apply function comes here.
row: A Pandas Series containing the a row in df.
"""
return row['a'] * row['b']
def functionUsedInMap(value):
""" This function is used in the map after the apply.
For this example, if the value is larger than 5,
return the cube, otherwise, return the square.
value: a value of whatever type is returned by functionUsedInApply.
"""
if value > 5:
return value**3
else:
return value**2
df['new_column_name'] = df.apply(functionUsedInApply,axis=1).map(functionUsedInMap)
The function above first adds columns a and b together and then returns the square of that value for a+b <=5 and the cube of that value for a+b > 5.
df['c'] = df['a'] * df['b']apply.